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Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management. [ABSTRACT FROM AUTHOR]

This study was carried out to understand the pattern of surface energy fluxes over a periodical scale and energy balance closure using Large Aperture Scintillometer and Micrometeorological tower. The standalone technique as 'Scintillometry' which observes the structure parameter of refractive index based on Monin-Obukhov Similarity theory, has the potential to measure the sensible heat flux precisely. This paper discusses the surface energy balance components and energy balance closure over a period of August 2017 to June 2018. The maximum mean energy fluxes Rn, G, H and LE were observed in September (98.6 Wm-2), May (13.9 Wm-2), June (53.3 Wm-2) and August (82.1 Wm-2), respectively. The overall mean ET was observed at the rate of 1.36 mm day-1 during the study period. This scintillometry technique may further use in evapotranspiration modelling from polar orbiting satellite to geostationary satellite over a heterogeneous and undulated landscape. [ABSTRACT FROM AUTHOR]

Accurate and quantitative regional estimates of the carbon budget require an integration of eddy covariance (EC) flux-tower observations and remote sensing in ecosystem models. In this study, a simple remote sensing driven light use efficiency (LUE) model was used to estimate the primary productivity for major cropping systems using multi-temporal satellite data over the Saharanpur district in India. The model is based on radiation absorption and its conversion into biomass. The LUE model was implemented for major crop rotations derived from the time-series of Sentinel-2 and Landsat 8 with monthly satellite-based spatially explicit fields of photosynthetically active radiation (PAR), fraction of absorbed PAR (fAPAR) and down-regulated light use efficiency. Incident PAR and fAPAR were estimated on monthly basis from the ground-calibrated empirical equation using INSAT-3D insolation product and remote sensing–based vegetation indices, respectively. Spatial LUE maps created by down-regulating maximum LUE (EC tower-based) with water and temperature stressors derived from land surface water index (LSWI) and EC-based cardinal temperature, respectively. LUE-based modeled GPP over the sugarcane-wheat system was found higher than the rice-wheat system in Saharanpur district. This is because C4 crop (sugarcane) has very high photosynthetic efficiency compared to C3 crops (rice and wheat). Modeled GPP over the sugarcane-wheat system was found in good agreement with observed EC tower-based GPP (Index of Agreement = 0.93). Further regionally calibrated remote sensing–based LUE model well captures gross photosynthesis rates (GPP) over cropland ecosystem compared to globally modeled MODIS GPP product. [ABSTRACT FROM AUTHOR]

In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing "crop management", "soil management", and "water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management. [ABSTRACT FROM AUTHOR]

The light use efficiency (LUE) approach is a well-established method for estimating gross primary productivity (GPP) over large areas using Earth observation data. The present study aims to determine maximum light use efficiency (LUEmax) values specific to the northwest Himalayan foothills of India. It also aims to estimate the spatio-temporal variability of GPP from 2001 to 2020 using remote sensing data in combination with eddy covariance data in the LUE-based model. The model was parameterized using different sets of default and calculated parameters. The study showed that the use of PFT-specific LUEmax and temperatures increased the accuracy of the model predictions. On validation, the LUE-based model predicted GPP showed R² = 0.82 for moist deciduous and R² = 0.83 for dry deciduous PFTs. The study revealed that with rigorous model parameterization, RS data can be used in an LUE-based model to achieve accurate spatio-temporal estimates of GPP. [ABSTRACT FROM AUTHOR]

The present study focusses on the estimation of ET using Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model over the Sal (Shorea robusta Gaertn.f) forest in the subtropical Doon Valley, India for the dry periods of 2016–18. Parameterization of METRIC model was done for surface temperature (Ts), momentum roughness length (zom), and surface albedo (α) estimations to make it suitable for Forest ET assessment. Results showed good agreement between the daily ET derived from the METRIC model and observed ET by eddy covariance method at Barkot Flux Tower (BFT) site. Results of parameterized METRIC model showed improvement over non-parameterized model with higher coefficient of determination (0.81), Nash Sutcliffe Efficiency (0.71) and lower Root Mean Square Error (0.48 mm). Average ET for the Sal forest ranged from 1.18-4.78 mm/day. Monthly and seasonal ET was derived using cubic spline interpolation-based time integration of reference ET fraction (ETrF). [ABSTRACT FROM AUTHOR]

Wilt caused by Fusarium oxysporum f. sp. cumini is the most serious disease of cumin (Cuminum cyminum L.) in India. Cumin germplasm was screened against Fusarium wilt at S. D. Agricultural University, Gujarat, India. Out of 105 genotypes evaluated, none of the genotype was found to be immune to wilt. The genotypes viz., JC- 18-11 and GC-5-1 were highly resistant with a minimum wilt incidence of 5.00 per cent while the genotypes viz., JC- 18-01 (17.25%), JC-18-07 (18.25%), GP-5 (20.05%) and GP-7 (20.00%) were resistant. The genotypes viz., JC-18-03 (25.15%), JC-18-05 (25.00%), JC-18-06 (25.00%), GP-3 (25.00%), GC-3(c) (25.06%), JC-2010-5 (30.25%), Sanand 5 (30.50%), Sanand 6(30.25%) and GP-4 (30.14%) were found moderately resistant. The genotypes viz., JC-18-04 (35%), GP-2 (35%), GC-5-2 (35%), GC-5 (c) (35%) JC-18-02 (40%), JC-2000-28-1 (40%), JC-16-07 (40%), JC-18-08 (45%), GC-4 (c) (45%) and J-Cum-2-2017 (50%) were found susceptible to the pathogen. The remaining germplasm accessions with the wilt incidence of more than 50% were susceptible to highly susceptible. [ABSTRACT FROM AUTHOR]

The present study aims to estimate the spatio-temporal variability of gross primary productivity (GPP) in moist and dry deciduous plant functional types (PFTs) of northwest Himalayan foothills of India using remote sensing-based Temperature-Greenness (TG) model and to study the response of GPP to environmental variables. TG model was implemented in Google Earth Engine platform using Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MOD13A2) and land surface temperature (MOD11A2) from 2001 to 2018. The mean monthly GPP ranged from 1.80 to 18.57 gCm−2day−1 in moist deciduous and from 0.20 to 12.06 gCm−2day−1 in dry deciduous PFTs. On site-scale validation with eddy covariance flux tower GPP, the modelled GPP showed R2=0.79 for moist deciduous and R2=0.77 for dry deciduous PFT. Leaf area index showed the highest correlation with the predicted GPP (r = 0.74 for moist and 0.83 for dry deciduous PFTs). The study revealed that TG model could predict the long-term forest GPP with minimum in-situ inputs. [ABSTRACT FROM AUTHOR]

Estimation of terrestrial gross primary productivity (GPP) is critical for global climate and ecological studies. However, the lack of multi-model studies for GPP estimation over agroecosystem in India limits the carbon budgeting at the regional scales. Satellite-derived parameters [(e.g., land surface temperature (LST), Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)] combined with meteorological variables offer a promising tool for regional estimates of the GPP. In this study, site-specific GPP was evaluated based on the eddy-covariance (EC) tower data and satellite-derived parameters. Four satellite-based GPP models, (a) greenness and radiation (GR) model, (b) VI × VI model, (c) photosynthetic capacity model (PCM), and (d) temperature and greenness (TG) model have been compared for the estimation of GPP in Saharanpur Flux tower site (SFS) from April 2014 to April 2015 using meteorological variables from EC and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. Among the predictive GPP models, TG models performed best with the RMSE of 2.03 g C m−2 day−1. The relationship of MODIS-LST with photosynthetically active radiation (PAR), GPP and air temperature (Ta) indicates that the climate variables are imperative for GPP estimation. In the VI × VI model series, the combination of EVI × EVI × PAR provided the best GPP estimates with an RMSE of 2.99 g C m−2 day−1. The comparative analysis of the GPP models has the potential for GPP estimates over agroecosystems and further carbon flux predictions at the regional scale. [ABSTRACT FROM AUTHOR]

In this study, CO2 exchange over sugarcane and wheat growing season was quantified by continuous measurement of CO2 fluxes using eddy covariance (EC) system from January 2014 to June 2015. We also elaborated on the response of CO2 fluxes to environmental variables. The results show that the ecosystem has seasonal and diurnal dynamics of CO2 with a distinctive U-shaped curve in both growing seasons with maximal CO2 absorption reaching up to −8.94 g C m−2 day−1 and −6.08 g C m−2 day−1 over sugarcane and wheat crop, respectively. The ecosystem as a whole acted as a carbon sink during the active growing season while it exhibits a carbon source prior to sowing and post-harvesting of crops. The cumulative net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco) were −923.04, 3316.65, and 2433.18 g C m−2 over the sugarcane growing season while the values were −192.30, 621.47, and 488.34 g C m−2 over the wheat growing season. The sesbania (green manure) appeared to be a carbon source once it is incorporated into soil. The response of day-time NEE to photosynthetically active radiation (PAR) under two vapor pressure deficit (VPD) sections (0–20 h Pa and 20–40 h Pa) seems more effective over sugarcane (R2 = 0.41–0.61) as compared to the wheat crop (R2 = 0.25–0.40). A decrease in net CO2 uptake was observed under higher VPD conditions. Similarly, night-time NEE was exponentially related to temperature at different soil moisture conditions and showed higher response to optimum soil moisture conditions for sugarcane (R2 = 0.87, 0.33 ≤ SWC < 0.42 m3 m−3) and wheat (R2 = 0.75, 0.31 ≤ SWC < 0.37 m3 m−3) crop seasons. The response of daily averaged NEE to environmental variables through path analysis indicates that PAR was the dominant predictor with the direct path coefficient of −0.65 and −0.74 over sugarcane and wheat growing season, respectively. Satellite-based GPP products from Moderate Resolution Imaging Spectroradiometer (GPPMOD) and Vegetation Photosynthetic model (GPPVPM) were also compared with the GPP obtained from EC (GPPEC) technique. The seasonal dynamics of GPPEC and GPPVPM agreed well with each other. This study covers the broad aspects ranging from micro-meteorology to remote sensing over C4-C3 cropping system [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) plays an indispensable role in the terrestrial carbon cycle and its dynamics. It also provides baseline data for developing sustainable management strategies in the region. In the present study, a decision-tree based random forest (RF) algorithm was used to estimate AGB for the different forest types in Doon valley, situated in the Himalayan foothills of India. Fifty-one spectral and textural variables were initially extracted using Landsat 8 Operational Land Imager and Sentinel-1A, which were further reduced to twenty optimum variables using the recursive feature elimination (RFE) method. These optimum variables were finally used to map AGB. Results showed that the spatial distribution of AGB ranged from 46.36 to 596.15 Mg ha−1 with good correlation (R2 = 0.87, RMSEr = 18.7%, RMSE = 62.56 Mg ha−1) between the observed and predicted AGB. This study validated the synergistic use of remote sensing, field data, and RF algorithm to precisely predict the spatial distribution of AGB. [ABSTRACT FROM AUTHOR]

India has committed to increase carbon sequestration by forests under the Nationally Determined Contributions (NDC). However, the CO2 carbon exchange characteristics (Gross Primary Productivity, Net Ecosystem Exchange, and Ecosystem respiration) of Indian forests are poorly understood. Immense carbon sequestration opportunities exist with the natural as well as planted forests. Therefore, it becomes vital to assess the potential of existing forests (natural and planted) by means of the most accurate and reliable methods of eddy covariance (EC). It is also important to know the eco-physiological response of the forests towards diurnal, seasonal, and yearly changes in the prevailing environmental conditions. We analyzed CO2 carbon exchange characteristics of a mature moist deciduous forest and a young mixed deciduous plantation located in the sub-tropical climate regime in the western Himalayan foothills of India with the help of co-located EC measurements (2016 to 2018). The study reveals that both the mature forest (− 719.43 gCm− 2year− 1) and young plantation (− 467.49 gC m− 2 year− 1) are absorbing a significant amount of atmospheric CO2 carbon. The maximum uptake of atmospheric CO2 occurred during the post-monsoon season in both the forests. The minimum NEE of the mixed deciduous plantation was observed during the leaf fall period while the NEE of the moist deciduous forest was the lowest in monsoon due to a higher respiration rate during monsoon. Among the environmental factors, soil moisture showed maximum control on the productivity of both the forest ecosystems. The value reported in this study can help Forest Survey of India in establishing/assessing emission factor for these particular forest types. This study can also help forest managers in carbon assessment of plantation under NDC. [ABSTRACT FROM AUTHOR]

Plant functional types (PFTs) have been widely used to represent the vegetation characteristics and their interlinkage with the surrounding environment in various earth system models. The present study aims to generate a PFT map for the Northwest Himalayan (NWH) foothills of India using seasonality parameters, topographic conditions, and climatic information from various satellite data and products using Random Forest (RF) algorithm in Google Earth Engine (GEE) platform. The seasonality information was extracted by carrying out a harmonic analysis of Normalized Difference Vegetation Index (NDVI) time-series (2008 to 2018) from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance 8 day 500 m data (MOD09A1). For topographic information, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) derived aspect and Multi-Scale Topographic Position Index (MTPI) were used, whereas, for climatic variables, WorldClim V2 Bioclimatic (Bioclim) variables were used. RF, a machine learning classifier, was used to generate a PFT map using these datasets. The overall accuracy of the resulting PFT map was found to be 83.33% with a Kappa coefficient of 0.71. The present study provides an effective approach for PFT classification using different well-established, freely available satellite data and products in the GEE platform. This approach can also be implemented in different ecological settings by using various meaningful variables at varying resolutions. [ABSTRACT FROM AUTHOR]

In this study, six commercial cherry tomato cultivars were analysed for variations in biochemical and technological parameters. Significant differences were observed among cultivars with respect to their technological and functional quality attributes. About 2.48-fold variation in titratable acidity, 2.47-fold variation in total phenolics, 4.13-fold variation in total carotenoid and 7.68-fold variation in lycopene contents were recorded. The total antioxidant activity and respiration rate were also found to vary about 1.85-fold and 1.48-fold, respectively. The highest antioxidant activity and total phenolic content were found in the Cherry tomato hybrid no.1 followed by Pusa cherry tomato-1. Hierarchical cluster analysis revealed that Cherry tomato hybrid no. 1, Pusa cherry tomato-1, and Nagmoti cultivars were abundantly rich in phytochemical and bioactive compounds. [ABSTRACT FROM AUTHOR]

Forests play a crucial role by regulating the global and local weather through the exchange of atmospheric gases and water vapor. The present study aims to study the intra-annual variability of net ecosystem exchange (NEE) of carbon dioxide in a sal (Shorea robusta) dominated moist deciduous forest in India by integrating eddy covariance (EC) data and Biome-Biogeochemical Cycle (Biome-BGC) model. The study also attempts to address the spatial variability of NEE with respect to phenology. Monthly average NEE were estimated using a calibrated Biome-BGC model and the spatial NEE was mapped using random forest (RF) regression algorithm. Phenology metrics were generated using the moderate resolution imaging spectrometer (MODIS) enhanced vegetation index product (MOD13A2) and its relationship with the estimated NEE was studied. RF regression model for monthly average spatial NEE estimation was built with an R2 of 0.84 and % RMSE of 3.68%. The study revealed that the NEE at the regional scale can be estimated using the basic meteorological variables like mean temperature, vapor pressure deficit, minimum temperature and total precipitation. Biome-BGC model output showed that the sal forest of the study area acted as a net sink of carbon in almost all months of 2015, except April to June. Peak NEE value (− 2.80 to − 2.96 g C m−2 day−1) was observed during October month. Annual NEE of sal forest in 2015 was found to be − 526.87 g C m−2 year−1. With the start of season (end of June), sal forest showed an increasing trend in NEE while decreasing trend was observed at the end of season (end of October). The study showed the applicability of Biome-BGC model in Indian forest when integrated with EC data. The study also highlighted the utility of RF in capturing the spatial variability of NEE over large area. [ABSTRACT FROM AUTHOR]

Estimation and monitoring of crop evapotranspiration (ETc) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ETc by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ETc was estimated 2.72 and 2.47 in mm day−1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ETc. [ABSTRACT FROM AUTHOR]

Net Primary Productivity (NPP) is a significant biophysical vegetation variable to understand the spatio-temporal distribution of carbon and source-sink nature of the ecosystem. This study was carried out in a forest plantation area and aimed to (i) estimate the spatio-temporal patterns of NPP during 2009 and 2010 using Carnegie-Ames-Stanford Approach [CASA] model and (ii) study the effects of climate variables on the NPP using generalized linear modelling (GLM) approach. The total annual NPP varied from 157.21 to 1030.89 gC m−2 yr−1 for the year 2009 and from 154.36 to 1124.85 g C m−2 yr−1 for the year 2010. The annual NPP was assessed across four major plantation types, where maximum NPP gain (106 and 139 g C m−2 yr−1 ) in October was noticed in teak (Tectona grandis) and minimum (77 and 109 g C m−2 yr−1 ) in eucalyptus (Eucalyptus hybrid) during 2009 and 2010.The validation, using field-estimated NPP, showed under-estimation of modelled NPP, with maximum MAPE of 34% for eucalyptus and minimum of 13% for teak. The dominant influence of precipitation on the NPP was revealed by GLM explaining more than 20% of variation. CASA model efficiently estimated the annual NPP of plantations. The accuracy could be improved further with inclusion of higher resolution data. [ABSTRACT FROM AUTHOR]

Various global circulation models predict a change in net irrigation requirements worldwide due to the impacts of climate change and in India, depending upon the region, irrigation requirements are likely to change by different magnitudes. The spatial distribution of trend in crop and irrigation water requirements of wheat projected for two climatic periods (2021-50 and 2051-80) across major wheat growing districts of the country were analyzed, making use of climate change projection data from NorESM1-M model of the CMIP5 in combination with RCP 4.5. Decreasing trends in water requirements were projected over 90 per cent of wheat growing districts in 2021-50, whereas increasing trends in crop and irrigation water requirement are expected over 95.4 per cent and 62.4 per cent areas, respectively in 2051-80 climatic periods. Results showed that decreasing/increasing trends projected in water requirements of wheat crop is due to change in crop growing period, which is projected to decrease across entire wheat growing area in 2021-50, whereas it is likely to increase over 78.2 per cent in 2051- 80 climatic periods. [ABSTRACT FROM AUTHOR]

Future climate change projections for India indicate distinct rise in temperature and increased variability in rainfall. This study aims to assess the impact of climate change on sorghum productivity in India in future climatic periods (2025, 2050 and 2075) using DSSAT-sorghum and suggest adaptation strategies to negate the negative impact of climate change on sorghum productivity in the future climates. Three CMIP-5 climate models (GFDL-ESM2M, MIROC5 and NorESM1-M) generated weather data for three future periods were used at various locations for kharif (Akola, Dharwad, Surat and Udaipur) and rabi (Bijapur, Dharwad, Rahuri and Solapur) seasons to simulate sorghum yields. Projected changes in day-night temperatures and rainfall during kharif and rabi growing seasons at these locations are diverse both in direction and magnitude. Increasing trend in rainfall is observed during both crop seasons towards the end of 21st century. Sorghum crop is likely to experience warmer temperature in the second half of the century and rise in minimum temperature is more explicit than maximum temperature at all the locations. Location specific management options can be adopted to mitigate the negative impacts of the change in climate in future projected scenarios, as they are found beneficial. [ABSTRACT FROM AUTHOR]

The network of eddy covariance (EC) based flux-towers is growing rapidly in India to accurately account carbon source/sink capacity of forest and managed ecosystems. Sal (Shorea robusta) forests representing about 16% of the total Indian forested area, a very important component of Indian carbon budget. One year observation on ecosystem scale CO2/H2O flux and other micrometeorological observations were analysed for elucidating diurnal and seasonal pattern of fluxes along with their response to biophysical and environmental factors of an Indian sal forest. Daily average air temperature ranged between 10.31 °C to 32.36 °C. Maximum Net Ecosystem Exchange (NEE) of -5.51 g C m-2 day-1 occurred on the 244 DOY due to ideal condition for photosynthesis. Environmental factors governing carbon flux components agreed with the incident of maximum and minimum NEE, Gross Primary Productivity (GPP) and Ecosystem Respiration (Re) in Barkot Flux Site (BFS). Clear variation in monthly NEE, GPP and Re was observed during the study. Higher LAI and sufficient moisture condition resulted to higher absorption of carbon during September-March. During pre-monsoon (April-June) NEE was lowered due to reduce LAI and high VPD. During the rainy season (July-August) the carbon sequestration potential was highly reduced due to cloudy condition and rain accelerated Re. Annual GPP, Re and NEE of BFS was 2916.19 g C m-2 yr-1, 2408.32 g C m-2 and -507.89 g C m-2 yr-1. [ABSTRACT FROM AUTHOR]

Forest plays a significant role in regulating the carbon budget and mitigating climate change in long term. However, lack of spatially explicit and accurate information on carbon exchange components from diverse forest ecosystem types in India limits carbon budgeting on a regional scale. Remote-sensing-driven ecosystem models are well-established tools for estimating gross primary productivity (GPP) over large areas but they are seldom found erroneous if implemented without proper calibration of biome-specific parameters. The present study evaluates the combined use of eddy covariance (EC) data and satellite-derived variables for estimating GPP over large areas. Four remote-sensing-driven models, (i) temperature–greenness (TG) model, (ii) greenness–radiation (GR) model, (iii) light use efficiency (LUE) model, and (iv) remote-sensing-based LUE (LUERS) model, were parameterized with EC measurements and compared with 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products for a moistShorea robustaforest in northern part of India. EC observed 8-day average GPP varied from 5.38 to 12.42 g C m−2day−1. Among the four tested models, TG model had the highest root mean square error (RMSE) of 1.28 g C m−2day−1, while GR and LUERSmodels had moderate RMSE of 0.99 g C m−2day−1and 0.98 g C m−2day−1, respectively. The closest GPP estimate was given by LUE model with RMSE of 0.93 g C m−2day−1.The RMSE for all four models were four times lower than that of MODIS GPP. Lower maximum LUE (and uncertainty in the environmental scalar used in MODIS GPP algorithm could have contributed to higher RMSE. More accurate modelling of GPP can help in better understanding of forest ecological functions with the changing climate. [ABSTRACT FROM PUBLISHER]

The spatial distribution and trend in rainfall and crop water requirement for sorghum during different time periods viz. baseline (1981-2010) and two future climate epochs (2021-2050 and 2051- 2080) over all major sorghum (kharif and rabi) growing regions of India was studied using climate change projections data from NorESM-1M model of the CMIP-5 in RCP4.5 scenario. A significant decreasing trend in kharif season rainfall is noticed during 2021-2050 over Madhya Pradesh. Majority of the kharif and rabi sorghum growing regions may receive more rainfall during 2051-2080. Increasing trend is projected for crop water requirement over majority of sorghum regions in both seasons with significant increase over the core growing area of rabi sorghum. Regions were identified where moisture stress is likely to be severe during kharif and rabi seasons. [ABSTRACT FROM AUTHOR]

Wheat is highly sensitive to climate change especially temperature changes experienced in the later phase of crop season. Hence, it is of immense importance to know how and to what extent climate change will affect wheat yields and to assess the adaptive strategies for mitigating possible negative consequences on wheat production. Wheat yield responses to three future climatic periods (2025, 2050 and 2075) were studied by driving DSSAT-Wheat (v4.5) model with daily weather from three CMIP-5 climate models' (GFDL-ESM2M, MIROC5, and NorESM1-M) as the basic input at four sites (Ludhiana, Raipur, Akola and New Delhi) representing three major wheat growing zones of the country. Projected changes in growing season (November-March) day and night temperatures at four sites differed substantially both in direction and magnitude. Day temperatures are projected to rise conspicuously at Ludhiana, representing northwest parts of the country, and moderately over central parts of India (Akola and Raipur). Positive rainfall anomalies at Ludhiana (+76%) and negative anomalies at Raipur (-15%) are projected in future climates. With these anticipated changes, wheat is likely to experience warmer days (+1.1 °C) at Ludhiana and nights at Raipur (+2.8 °C) and more seasonal moisture availability at Ludhiana in future climates. Negative impacts of climatic change in these sites are found to be minimized by adapting one or a combination of management practices, which are site specific. [ABSTRACT FROM AUTHOR]

Integrated Nutrient Management (INM) implies the most efficient use and management of organic, inorganic and biological sources of major nutrients as well as micronutrients to attain higher levels of crop productivity and to maintain the fertility of the soil. Brinjal is one of the most popular and commercially important vegetable crops of India. Research works on INM system for brinjal crop is scanty. This paper has reviewed the research work carried out by various scientists in India and abroad. The review deals with the effect of different INM components on growth, yield, fruit quality, nutrient content and uptake, incidence of brinjal shoot and fruit borer (BSFB) as well as economics of brinjal and other Solanaceous vegetables. [ABSTRACT FROM AUTHOR]

The current study was planned to identify the epidemiological factors associated with leptospirosis in South Gujarat region using neighborhood controls.~Background~Background~A total of 100 cases of leptospirosis occurred in South Gujarat region during the year 2012 were selected using simple random sampling. Three neighbors of the selected cases formed the controls (n = 300). A pretested structured questionnaire was used for data collection and data were analyzed using Epi Info 2007.~Methods~Methods~There was significant association of illiteracy (odds ratio [OR] =1.82, 95% confidence interval [CI] =1.14-2.89), working in waterlogged fields during the reference season (OR = 4.6, 95% CI = 1.6-17.9), swimming/bathing in canals, open air defecation practices, storage of cow dung in or surrounding house, residence in the house made up of cow dung walls, households with access of food to rodents, injuries over hands/foot during the endemic season (OR = 3, 95% CI = 1.8-4.8), and history of skin disease during the endemic season (OR = 4.2, 95% CI = 2-8.5), with leptospirosis. Only 10% of individuals had gumboots for protection. A total of 83 (83%) cases and 240 (80%) controls had taken oral doxycycline chemoprophylaxis (P > 0.05). Cases had taken chemoprophylaxis for a median 4 weeks (range: 1-8) while controls had taken the same for median 8 weeks (range = 1-8) (P < 0.002).~Results~Results~Although the commonly established factors appear to be associated with leptospirosis, the role of host factors seems to play a more important role in determining susceptibility to leptospirosis in exposed individuals.~Conclusions~Conclusions [ABSTRACT FROM AUTHOR]

This study examines the consistency between the AVHRR and MODIS normalized difference vegetation index (NDVI) datasets in estimating net primary productivity (NPP) and net ecosystem productivity (NEP) over India during 2001-2006 in a terrestrial ecosystem model. Harmonic analysis is employed to estimate seasonal components of the time series. The stationary components (representing long-term mean) of the respective NDVI time series are highly coherent and exhibit inherent natural vegetation characteristics with high values over the forest, moderate over the cropland, and small over the grassland. Both data exhibit strong semi-annual oscillations over the cropland dominated Indo-Gangetic plains while annual oscillations are strong over most parts of the country. MODIS has larger annual amplitude than that of the AVHRR. The similar variability exists on the estimates of NPP and NEP across India. In an annual scale, MODIS-based NPP budget is 1.78 PgC, which is 27 % higher than the AVHRR- based estimate. It revealed that the Indian terrestrial ecosystem remained the sink of atmospheric CO during the study period with 42 TgC y NEP budget associated with MODIS-based estimate against 18 TgC y for the AVHRR-based estimate. [ABSTRACT FROM AUTHOR]

Monitoring of drought and associated agricultural production deficit using meteorological indices is essential component for drought preparedness. Remote sensing-based NDVI also plays a key role in drought assessment, but alone it fails due to time lag of 3-4 weeks. In view of improving drought monitoring in Bundelkhand region of India, it was proposed to use combination of meteorological and remote sensing-based approach. The study aims to monitor and assess inter-annual variability in spatial drought and related crop loss in Bundelkhand region using time series of daily rainfall of Climate Prediction Centre (NOAA) and SPOT-VGT-based normalized difference vegetation index. Instead of NDVI, vegetation condition index (VCI) was used to normalize geographical differences in vegetation types and physiographical setting. The new approach is linear weighted index called spatial vegetation drought index (SVDI) constructed from VCI derived from SPOT-VGT and meteorological index named rainfall anomaly index (RAI) for monitoring short-term drought stress in Bundelkhand region. The spatial and temporal pattern of drought matches well with RAI. VCI found to be significantly related to drought stress in terms of rainfall anomaly for majority of decades as well as crop yield anomaly of both food grains and pulses. A modified rainfall anomaly (MRAI) was also constructed by assigning weights to RAI of past three decades to normalize the residual moisture status. The newly formulated SVDI obtained by integrating MRAI and VCI improved the spatial prediction of drought and to detect crop loss associated with short-term drought stress. Comparing real-time drought condition from the observation around the concerned area showed that SVDI was able to illustrate drought stress on large-scale efficiently and can give information about the departure in crop productivity when correlated with crop yield anomaly. [ABSTRACT FROM AUTHOR]

Objective:This study aims to understand the demographics, mode of trauma, hospital stay, complications, neurological improvement, mortality and expenditure incurred by Indian patients with spinal trauma and ankylosing spondylitis (AS).Methods:Retrospective analysis of the patient data admitted to a tertiary referral hospital from 2008 to 2013 with the diagnosis of AS and spinal trauma was carried out. The variables studied were demographics, mode of trauma, neurological status, neurological improvement, involved vertebral level, duration of hospital stay, comorbid factors, expenditure and complications during the stay.Results:Forty-six patients with diagnosis of AS with spine trauma were admitted over the last 5 years with a total of 52 fractures. All were male patients; 58.6% had injury because of trivial trauma and 78.2% patients presented with neurological injury. C5 C6, C6 C7, C7 D1 and D12 were the most common injured level. Fractures through intervertebral disc were most common in cervical spine. Of the patients, 52.7% had shown neurological improvement of at least grade 1(AIS). Mean expenditure of patient admitted with spinal cord injury (SCI) with AS is 7957 USD (United States dollar), which is around five times the per capita income in India (as per year 2013).Conclusion:Males with AS are much more prone to spinal fractures than females and its incidence may be higher than previously reported. Domestic falls are the most common mechanism of spinal trauma in this population. High velocity injuries are associated with complete SCI. The study reinforces the need for development of subsidized spinal care services for SCI management. [ABSTRACT FROM AUTHOR]

Using satellite observations of Normalized Difference Vegetation Index together with climate data from other sources in a terrestrial biosphere model, inter-annual variability of Net Primary Productivity (NPP) over India during 1981-2006 was studied. It is revealed that the variability is large over mixed shrub and grassland (MGL), moderate over cropland and small over the forest regions. Inter-annual variability of NPP exhibits strong positive coherence with the variability of precipitation, and weak coherence with the variability of temperature and solar radiation. Estimated linear growth rate of annual NPP is 0.005 Pg C Yr−2 which is equivalent to 8.5% over the country during past 25 years. This increase is primarily due to the enhancement of productivity over agricultural lands in the country. NPP has increased over most parts of the country during the early 15-year period (1981-1995) resulting in a 10% growth rate of national NPP budget. On the other hand, the NPP growth rate has been reduced to 2.5% during later 15 years period (1991-2005) owing to large decline of NPP over the Indo-Gangetic plains. Climate had a strong control on NPP growth rate during both the periods. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

An attempt has been made to assimilate remotely sensed input data in mechanistic crop simulation model World Food Studies (WOFOST) for in-season wheat yield forecasting in Punjab state of India. Spatial weather data at ‘5 km × 5 km’ grid were generated through interpolation of daily available weather data. Grid-wise sowing date was estimated from time-series normalized difference vegetation index (NDVI) data product from vegetation sensor of SPOT satellite (SPOT-VGT). The leaf area index (LAI) derived from remotely sensed data was used in the simulation model WOFOST for predicting spatial yield. The simulated wheat grain yield for each grid was aggregated to district level using the actual wheat fraction for each grid derived from remote sensing-based wheat crop map. A comparison was made between the estimated yield and that reported by Department of Agriculture. The procedure was repeated for three crop seasons to check the reliability. The results indicated that this technique could be used for spatial yield prediction at regional level with a root mean square error (RMSE) of <0.4 tonnes ha−1 at state level. [ABSTRACT FROM AUTHOR]

Net photosynthesis rate (PN) has an important role in controlling the ecosystem primary production and it is important to understand the relationships between PN and other physiological and environmental variables to improve and develop models for predicting plant growth and productivity. Here we study utility of ground-based hyperspectral measurements in estimating net photosynthesis during the growth cycle of rice. Ground-based hyperspectral reflectance and micrometeorological data (PN, chlorophyll concentration index (CCI), photosynthetically active radiation (PAR)) were collected from a rice field on three dates in the kharif season at Meerut, Uttar Pradesh, India. Green chlorophyll index (CIg) showed a positive and statistically significant relationship with CCI (R2 = 0.64). Use of single index showed poor fit with PN. The model based on a combination of two variables (vegetation index (VI) × PAR) however proved better in explaining the variance of PN. Among the VI-based combinations comprising three variables which depend on the Monteith light-use efficiency (LUE) concept, models consisting of either CIg or CCI as one variable appeared to be the best in predicting PN with R2 of nearly 0.6. The study concludes that a single index is insufficient to account for factors controlling variation in PN estimation whereas Monteith logic-based combinations have shown its potential for PN estimation by incorporating the chlorophyll based and other vegetation indices as the proxy of LUE and fAPAR (fraction of absorbed photosynthetically active radiation) respectively. [ABSTRACT FROM AUTHOR]

In the present study, the Carnegie-Ames-Stanford Approach (CASA), a terrestrial biosphere model, has been used to investigate spatiotemporal pattern of net primary productivity (NPP) during 2003 over the Indian subcontinent. The model drivers at 2-min spatial resolution were derived from National Oceanic and Atmospheric Administration advanced very high resolution radiometer normalized difference vegetation index, weather inputs, and soil and land cover maps. The annual NPP was estimated to be 1.57 Pg C (at the rate of 544 g C m), of which 56% contributed by croplands (with 53% of geographic area of the country (GAC)), 18.5% by broadleaf deciduous forest (15% of GAC), 10% by broadleaf evergreen forest (5% of GAC), and 8% by mixed shrub and grassland (19% of GAC). There is very good agreement between the modeled NPP and ground-based cropland NPP estimates over the western India ( R = 0.54; p = 0.05). The comparison of CASA-based annual NPP estimates with the similar products from other operational algorithms such as C-fix and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate that high agreement exists between the CASA and MODIS products over all land covers of the country, while agreement between CASA and C-Fix products is relatively low over the region dominated by agriculture and grassland, and the agreement is very low over the forest land. Sensitivity analysis suggest that the difference could be due to inclusion of variable light use efficiency (LUE) across different land cover types and environment stress scalars as downregulator of NPP in the present CASA model study. Sensitivity analysis further shows that the CASA model can overestimate the NPP by 50% of the national budget in absence of downregulators and underestimate the NPP by 27% of the national budget by the use of constant LUE (0.39 gC MJ) across different vegetation cover types. [ABSTRACT FROM AUTHOR]

Argyreia speciosa (Linn. f.) Sweet is a popular Indian medicinal plant, which has long been used in traditional Ayurvedic Indian medicine for various diseases. This plant is pharmacologically studied for nootropic, aphrodisiac, immunomodulatory, hepatoprotective, antioxidant, antiinflammatory, antihyperglycemic, antidiarrheal, antimicrobial, antiviral, nematicidal, antiulcer, anticonvulsant, analgesic and central nervous depressant activities. A wide range of phytochemical constituents have been isolated from this plant. A comprehensive account of the morphology, phytochemical constituents and pharmacological activities reported are included in view of the many recent findings of importance on this plant. [ABSTRACT FROM AUTHOR]

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Present communication reports the physico-chemical and biological quality of seawater and status of benthos of a highly industrialized shore of the north-western coastline of India. The coastal area considered for the present study, encircled by a variety of industries, was divided into two sampling sites and monitored for two consecutive years. Results of the water quality suggest that the obtained values of the physical and chemical parameters of seawater were comparable with data reported earlier. However, data obtained in the biological parameters of the seawater showed a declining trend. Results of the intertidal macrofaunal diversity studies revealed that the muddy upper littoral zones were represented by few species of coelenterata, porifera, arthropoda and mollusca. In the rocky-muddy middle littoral zones, gastropods, stars fishes, corallites, crabs, polychetes and tubeworms were present, whereas, predominantly rocky lower littoral zones were comparatively rich in macrofaunal diversity with small patches of coral colonies. However, when the results obtained in the present study was compared with that of earlier reported data, it was clear that the macrofaunal diversity indeed declined considerably over the years. This may be due to habitat destruction and habitat alteration in the coastline caused by increased anthropogenic activities in the area. Seasonal variations in the population density and abundance were observed in most of the faunal groups except in sessile corals and sponges. This may be due to local migration of the faunal groups towards deeper regions of the Gulf, as supported by the analysis of similarity, to avoid influx of freshwater during monsoon, and high temperature during summer and post monsoon seasons. The overall assessment of different parameters of this study revealed that though the physico- chemical characteristics of the seawater did not varied much from the earlier reported status, the biological characteristics of the seawater and intertidal zone was affected possibly by a high degree of anthropogenic pressure. [ABSTRACT FROM AUTHOR]

Leaves of Actinodaphne hookeri Meissn (Family Lauraceae; local name: Pisa) has been in use traditionally for the treatment of diabetes and disorders of the urinary tract which are more common in Chattisgarh and eastern part of India. In the present study, leaves of A. hookeri were subjected to phytochemical investigation and evaluated for anti-diabetic activity. The ethanol and the chloroform extract were found to have significant (p<0.01) blood glucose lowering effect. The extracts also significantly (p<0.01) lowered the increased serum cholesterol and low density lipoprotein levels. Preliminary phytochemical investigation revealed the presence of alkaloids, flavonoids, triterpenoids and glycosides as the major constituents in the ethanol extract. The chloroform extract also showed significant (p<0.01) antihyperglycemic activity and contained alkaloids and triterpenes. It is concluded that the antidiabetic activity of A. hookeri may be due to the presence of alkaloids and triterpenes, and might be promising for the development of phytomedicine for diabetes mellitus along with its associated complica…. [ABSTRACT FROM AUTHOR]

In the hilly areas of eastern Gujarat, western Madhya Pradesh and southern Rajasthan, in western India, farmers are very resource-poor and cultivate small and fragmented land holdings. Maize is their main rainy season (kharif) cereal and it is grown as a rainfed crop in low-fertility fields, often on sloping land that is vulnerable to soil erosion. Its productivity is very low, averaging below 1 t ha−1. New farm technologies to increase this productivity have to be low cost to be attractive to farmers who have limited access to purchased inputs and few means to purchase them. From observations of local farming practices, intercropping of maize with legumes was identified as an attractive option because the only additional input needed is seed of the legume crop. Participatory research was conducted on intercropping of maize with improved varieties of horsegram (Macrotyloma uniflorum). Many farmers who tried this intercropping adopted it in subsequent years, while others preferred to grow the new horsegram varieties as a sole crop. Farmers reported that less weeding was required in the intercrop as the horsegram smothered weeds. All farmers used the dry stover from the horsegram as a fodder for their animals. Farmers used the whole seed as dal, which provided additional protein in their diet. Farmers also sold the grain, but it fetched a low price in the poorly developed market for horsegram. Previously intercropping had been tried with local landraces, but the acceptance of intercropping was higher with new varieties such as AK-42 that yielded over 60% more grain. Participatory trials in which only one entry was compared with the local variety did not show a difference between AK-21 and AK-42 as in all cases both were preferred over the local variety. When they were directly compared with each other, farmers' perceptions showed a significant preference for AK-42. Variety IVH-2 was found to be better than AK-42: it matured 15 days earlier, better matching the maturity of the maize, had superior grain quality and yielded about the same. The greater uptake of improved horsegram varieties for sole and intercropping is likely to be limited by the lack of seed supply. [ABSTRACT FROM PUBLISHER]

Clear-sky dekadal relative evapotranspiration (RET) was derived using the surface energy-balance approach applied to 10-day composite NOAA PAL (8 km×8 km) datasets over the Indian landmass. This was further used to differentiate between growth characteristics for an irrigated intensive agriculture over a northern India state (e.g. Punjab) and a rainfed ill-posed agriculture over a central India state (e.g. Madhya Pradesh) using time-series data sets for five growing years (June-April): 1996-1997, 1997-1998, 1998-1999, 1999-2000, and 2000-2001. The triangular scatter between RET and normalized difference vegetation index (NDVI) showed that the minimum RET increases linearly with NDVI producing a 'basal line' that represents relative canopy transpiration only. A clear distinction in scatter was found between the two contrasting agro-ecosystems showing a higher RET or root zone wetness in irrigated than rainfed systems. In rainfed rice-growing regions, an inverse correlation (0.6-0.75) was found between RET and the Keetch-Byram meteorological drought index (KBDI), and a substantial reduction in RET was also found in a sub-normal (2000) compared with a normal (1999) monsoon season. RET estimates were found to be most sensitive to atmospheric transmissivity followed by other land-surface radiation budget inputs, such as NDVI, LST, and albedo. Error propagation due to three surface parameters is the opposite of that for transmissivity. The maximum possible error in clear-sky NOAA PAL RET was estimated to be 12-15%. This test study would be helpful in deriving RET using optical and thermal data from a suite of current and future Indian geostationary satellite sensors for monitoring growing conditions. [ABSTRACT FROM AUTHOR]

Regional estimates of crop yield are critical for a wide range of applications, including agricultural land management and carbon cycle modelling. Remotely sensed images offer great potential in estimating crop extent and yield over large areas owing to their synoptic and repetitive coverage. Over the last few decades, the most commonly used yield–vegetation index relationship has been criticized because of its strong empirical character. Therefore, the present study was mainly focused on estimating regional wheat yield by remote sensing from the parametric Monteith's model, in an intensive agricultural region (Haryana state) in India. Discrimination and area estimates of wheat crop were achieved by spectral classification of image from AWiFS (Advanced Wide Field Sensor) on‐board the IRS‐P6 satellite. Remotely sensed estimates of the fraction of absorbed photosynthetically active radiation (fAPAR) and daily temperature were used as input to a simple model based on light‐use efficiency to estimate wheat yields at the pixel level. Major winter crops (wheat, mustard and sugarcane) were discriminated from single‐date AWiFS image with an accuracy of more than 80%. The estimates of wheat acreage from AWiFS had less than 5% relative deviation from official reports, which shows the potential of single‐date AWiFS image for estimating wheat acreage in Haryana. The physical range of yield estimates from satellites using Monteith's model was within reported yields of wheat for both methods of fAPAR, in an intensive irrigated wheat‐growing region. Comparison of satellite‐based and official estimates indicates errors in regional yields within 10% for 78% and 68% of cases with fAPAR_M1 and fAPAR_M2, respectively. However, wheat yields in general are over‐ and underestimated by the fAPAR_M1 and fAPAR_M2 methods, respectively. The validation with district level wheat yields revealed a root mean square error of 0.25 and 0.35 t ha-1 from fAPAR_M1 and fAPAR_M2, respectively, which shows the better performance of the fAPAR_M1 method for estimating regional wheat yields. Future work should address improvement in crop identification and field‐scale yield estimation by integration of high and coarse resolution satellite sensor data. [ABSTRACT FROM AUTHOR]

The article reports on precision farming technologies for sustainable agriculture in India. In the new millennium, explosive growth of population and signs of declining productivity puts great challenge against Indian agricultural sector to achieve food and environmental security. To meet this challenge, modem frontier technologies involving systems approach towards efficient crop and input management, and scientific land and water use planning, is an urgent need of India. Agricultural sustainability in India can be achieved only if the natural resource base upon which it relies is well managed. However, in attempt to achieve high productivity over past 3-4 decades, a critical linkage between agriculture and the environment fragility has been ignored resulting in agro-ecosystems with little resilience. Intensive use of chemical fertilizers and pesticides combined with poor management of watersheds has resulted in severe water stress, pesticides contamination of water and agricultural products in addition to the unacceptable degradation of soils resulting into disruption of ecosystems over larger areas. Hence, for developing country like India, adoption of environment-friendly technologies and holistic approach of farm management is, therefore, essential for agricultural sustainability. The extent and rate of change occurring in the development of information technologies have paved the way for significant change in crop production management and agricultural decision-making.

High-spectral-resolution data of the Airborne Imaging Spectrometer (AIS) developed by the Indian Space Research Organisation were analysed to check their potential for extraction of information concerning crop growth parameters. Reflectance spectra of wheat plots at different growth stages were generated to study the position of red edge. The shift of inflection wavelength towards longer wavelength was observed with increase in Leaf Area Index (LAI) and chlorophyll content. For the range of LAI from 0.08 to 3.16 and chlorophyll content from 2 to 39 (gm[sup -2]), the shift of inflection wavelength was observed in the range of 713-723 nm. A linear relationship was observed between the inflection wavelength and LAI as well as chlorophyll content. [ABSTRACT FROM AUTHOR]

The cold chain plays a major role in the universal immunization programme which helps in preventing against six major killer diseases in children. We collected 144 study samples randomly from different parts of Bangalore to know the training status of personnel, refrigeration facilities, storage, monitoring and potency of vaccines. It was observed that 6.6% of general practitioners were trained under Universal Immunization Programme, monitoring was not satisfactory, and two of the OPV samples from medical practitioners had an unsatisfactory titre dose. Comprehensive orientation/training on cold chain is essential for medical practitioners and other professionals. [ABSTRACT FROM AUTHOR]

The stem bark of Ficus bengalensis is reported to have antimicrobial activity, we planned our work as to evaluate and compare antimicrobial activity of alcoholic extracts of stem bark of F. bengalensis by cup and plate method collected from three different geographical regions viz. Delhi, Gujarat and Uttaranchal. The activity of all the extracts at the dose level 1000 μg/ml, 500 μg/ml and 100 μg/ml were found to be quiet different and all the extracts showed that activity is varied as geography and environmental conditions are changed. [ABSTRACT FROM AUTHOR]

A survey work on breeding practices followed in dairy animals of rural area in Sabarkantha district of North Gujarat was carried out. One hundred respondents of 10 villages having elite dairy animals were included in the study. Data revealed that 100 per cent farmers observed only mucus discharge and bellowing as a sole symptom of heat detection. Almost all farmers used AI for breeding their crossbred cows, while for buffaloes 13.00 and 5.00 per cent farmers followed AI and nature breeding, respectively, and the remaining 82.00 per cent farmers opted for both A.I. and NS. The AI/NS was followed between 12 and 24 hours after heat detection by all the respondents. Results revealed that majority of cows (82.18%) and buffaloes (90.00%) had = 15 months and 18 months of calving intervals, respectively. Poor result of AI (16.00%) and higher incidence of repeat breeding (22.00%) in buffaloes were the main constraints faced by the farmers. Better awareness of farmers regarding breeding of dairy animals was observed due to co-operative system in the district. [ABSTRACT FROM AUTHOR]